GRAM: KAN Meets Gram Polynomials
GRAM is inspired by Kolmogorov-Arnold Networks (KAN) alternatives like TorchKAN and ChebyKAN. GRAM introduces a simplified version of the KAN model but leveraging the simplicity of Gram polynomial transformations. What sets it apart from other alternatives is its unique characteristic of being discrete. Unlike other polynomials that are defined on a continuous interval, Gram polynomials stand out as they are defined on a set of discrete points. This discrete nature of GRAM offers a novel way to handle discretized datasets like images, and text data.
Table of Contents
Description
Benchmarks
In this repository, we conduct a series of benchmarks to evaluate different machine learning models on the MNIST dataset. The benchmarks are designed to test each model's accuracy, the number of parameters, and the speed of convergence over a total of 10 epochs. In our benchmarking process, we ensure a level playing field by enforcing a uniform architecture across all models. This architecture comprises of the following hidden layers:
- [Layer I] (28 * 28, 32)
- [Layer II] (32, 16)
- [Layer III] (16, 10)
Addition to the standard learning rate of 1e-3
Figure 1: Total Parameters
Figure 2: Accuracy
Figure 3: Convergence Speed
Current Issues
What's Next
License
MIT License
Copyright (c) [2024] [Khochawongwat Kongpana]
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